Existing approaches to explainable (or interpretable) ML broadly form two categories [1]}.
The first category are methods that are considered as intrinsically interpretable [2]}, [3]}, [4]}.
A prominent example for this class of explainable methods is linear regression which many consider
as interpretable due to the additive combination of weighted features. The weights for each feature
are interpreted as a measure for the importance or relevance of that feature.
